code: def create_multi_input_output_model(self):
      data1 = tf.keras.layers.Input(shape=[10])
      data2 = tf.keras.layers.Input(shape=[10])
  
      x1 = tf.keras.layers.Flatten()(data1)
      x1 = tf.keras.layers.Dense(10, activation="relu")(x1)
      pred1 = tf.keras.layers.Dense(2, activation="softmax")(x1)
  
      x2 = tf.keras.layers.Flatten()(data2)
      x2 = tf.keras.layers.Dense(10, activation="relu")(x2)
      pred2 = tf.keras.layers.Dense(2)(x2)
  
      model = tf.keras.models.Model(
          inputs=[data1, data2], outputs=[pred1, pred2]
      )
      model.compile(
          optimizer="rmsprop",
          loss=["sparse_categorical_crossentropy", "mse"],
      )
      return model
  
imports: import pytest
  from bigdl.orca.test_zoo_utils import ZooTestCase
  import tensorflow
  import numpy
  import os
  from bigdl.orca.tfpark import KerasModel, TFDataset, TFOptimizer
label_desc: Use image node_id 3 for calling the TestTFParkModel obj's underlying member method code with example usage: obj.create_multi_input_output_model() and returns: model